Abstract：
Composted materials serve as an effective soil nutrient amendment. Organic matter in compost plays an important role in quantifying composted materials overall quality and nutrient content. Measuring organic matter content traditionally takes considerable time, resources, and various laboratory equipment (e.g., oven, muffle furnace, crucibles, precision balance). Much like the quantitative color indices (e.g., sRGB R, sRGB G, sRGB B, CIEL*a* b*) derived from the low-cost NixPro2 color sensor have proven adept at predicting soil organic matter in-situ, the NixPro2 color sensor has the potential to be effective for predicting organic matter in composted materials without the need for traditional laboratory methods. In this study, a total of 200 compost samples (13 different compost types) were measured for organic matter content via traditional loss-on-ignition (LOI) and via the NixPro2 color sensor. The NixPro2 color sensor showed promising results with an LOI-prediction model utilizing the CIEL*a* b* color model through the application of the Generalized Additive Model (GAM) algorithm yielding an excellent prediction accuracy (validation R2 = 0.87, validation RMSE = 4.66 %). Moreover, the PCA scoreplot differentiated the three lowest organic matter compost types from the remaining 10 compost types. These results have valuable practical significance for the compost industry by predicting compost organic matter in real time without the need for laborious, time-consuming methods.
摘要：
堆肥材料可作为有效的土壤养分改良剂。堆肥中的有机物在量化堆肥材料的整体质量和营养成分中起着重要作用。传统上，测量有机物含量需要相当长的时间，资源,和各种实验室设备(例如，烤箱,马弗炉,坩埚,精密天平)。很像定量颜色指数(例如，sRGBR,sRGBG,sRGBB,CIEL*a*b*）源自低成本的NixPro2颜色传感器已被证明擅长原位预测土壤有机质，NixPro2颜色传感器有可能有效地预测堆肥材料中的有机物，而无需传统的实验室方法。在这项研究中,通过传统的烧失量（LOI）和NixPro2颜色传感器测量了总共200个堆肥样品（13种不同的堆肥类型）的有机物含量。NixPro2颜色传感器通过应用广义加法模型（GAM）算法，利用CIEL*a*b*颜色模型的LOI预测模型显示出了有希望的结果，从而产生了出色的预测精度（验证R2=0.87，验证RMSE=4.66％）。此外，PCA记分图将三种最低的有机质堆肥类型与其余10种堆肥类型区分开。这些结果对堆肥工业具有重要的现实意义,无需费力,耗时的方法。
